Healthcare Analytics Adoption Model
Read a More In-Depth Explanation
Why Healthcare Analytics?
Healthcare in the United States and other parts of the world has slowly been progressing through three waves of data management: data collection, data sharing, and data analytics. So far, the data collection and sharing waves, characterized by the urgent deployment of EHRs and health information exchanges, have failed to significantly impact the quality and cost of healthcare. In some cases, this nonstop emphasis on data has contributed to an over focus on the EHR, contributing to provider burnout and time and attention away from patients.
Despite the current hype about big data being the next “big” thing in other industries, in healthcare we are just beginning to have the necessary analytics capabilities that enable system-wide quality improvement and cost reduction efforts.
Healthcare analytics is the systematic use of data to create meaningful insights. The real promise of analytics lies in its ability to transform healthcare into a data-driven culture, powered by a world-class analytics platforms, like the Health Catalyst Data Operating System (DOS™).
The Healthcare Analytics Adoption Model
Healthcare data and analytics can be confusing and overwhelming without a framework to guide your approach and priorities. Because the healthcare industry lacked a comprehensive analytics model that fit the unique needs of healthcare data, a group of cross-industry healthcare veterans created the Healthcare Analytics Adoption Model.
Health organizations can reference the model throughout its analytics journey, as it provides specific guidance on classifying groups of analytics capabilities and provides systematic sequencing to adopting analytics within the health organization. It is critical for health systems to follow some type of analytics model because the right model will lay the foundation for a successful, sustainable analytics strategy that will support more complex data needs in the future.
A Framework to Develop Analytics Maturity
The Healthcare Analytics Adoption Model has evolved from a 5-step framework in 2002 to an 8-step model in 2012, combining lessons learned from analytics experts at Health Catalyst and Healthcare Information and Management Systems Society (HIMSS). Finally, in 2019, the latest version of the Healthcare Analytics Adoption Model, very similar to the original model, was released with an added level 9—a focus on developing patients’ analytics understanding so that patients and their care teams are making data-driven decisions together—to adapt to the ever-changing needs integrating data into healthcare.
The Healthcare Analytics Adoption Model provides three major benefits to health systems looking to grow in analytics maturity:
- A framework for evaluating the industry’s adoption of analytics.
- A roadmap to measure progress toward analytic adoption.
- A framework for evaluating vendor products.
When health systems leverage the Healthcare Analytics Adoption Model to its full potential, and follow it closely step by step, they will fully understand and leverage the capabilities of their analytics and achieve the ultimate goal that has eluded most provider organizations—improve the quality of care while lowering costs and enhancing clinician and patient satisfaction.
The Nine Levels of the Analytics Adoption Model
(click to reveal each level)
Level 9 – Direct-to-Patient Analytics & AI
Level 8 – Personalized Medicine & Prescriptive Analytics
Level 7 – Clinical Risk Intervention & Predictive Analytics
Level 6 – Population Health Management & Suggestive Analytics
Level 5 – Waste & Care Variability Reduction
Level 4 – Automated External Reporting
Level 3 – Automated Internal Reporting
Level 2 – Standardized Vocabulary & Patient Registries
Level 1 – Enterprise Data Warehouse
Foundation of data and technology
Enterprise Data Warehouse:
- At a minimum, the following data are co-located in a single data warehouse, locally or hosted: HIMSS EMR Stage 3 data, Revenue Cycle, Financial, Costing, Supply Chain, and Patient Experience.
- Searchable metadata repository is available across the enterprise.
- Data content includes insurance claims, if possible.
- Data warehouse is updated within one month of source system changes.
- Data governance is forming around the data quality of source systems.
- The EDW reports organizationally to the CIO.
Level 0 – Fragmented Point Solutions
Learn More About Each Level
- View Dale’s Healthcare Analytics Adoption Model webinar and download his presentation slides and transcript.
- Read an in-depth Healthcare Analytics Adoption Model article with a detailed explanation of each of the nine levels.
Find Out Where You Stand: Take the Self-Assessment Survey
Similar to the HIMSS EHR Adoption Model, there is a needed logical progression to become a systemic, analytics-driven organization. Health systems that aspire to higher-level results which attempt to tackle analytics in a scattered way are often frustrated by the lack of inadequate foundational platforms, tools, and skills that need to be mastered at the lower levels of the analytics adoption model. Based on our years of collective experience, we have designed a self-assessment survey to help you assess where your organization is consistently operating in each of the levels. Upon finishing the survey, you will receive a customized report either in HTML format or a PDF version that you can email to yourself, summarizing your status and a list of customized recommendations, based on your input.
Healthcare Analytics Is Evolving
The Health Catalyst Data Operating System (DOS™) is a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform. DOS™ is our response to a future of healthcare centered around the broad and more effective use of data.
Read More About Healthcare Analytics
The Analytics Adoption Model White Paper
Dale Sanders, Chief Technology Officer
Bridging the Data and Trust Gaps: Why Health Catalyst Entered the Life Sciences Market
Dale Sanders, Chief Technology Officer
The Homegrown Versus Commercial Digital Health Platform: Scalability and Other Reasons to Go with a Commercial Solution
Dale Sanders, Chief Technology Officer
The Healthcare Analytics Ecosystem: A Must-Have in Today’s Transformation
John Wadsworth, Vice President, Technical Operations
The Six Biggest Problems with Homegrown Healthcare Analytics Platforms
Ryan Smith, Senior Vice President and Executive Advisor
Three Must-Haves for a Successful Healthcare Data Strategy
David Grauer, Senior Vice President, Professional Services
Two Helpful Webinars
The Analytics Adoption Model Explained (On Demand Webinar, Slides, and Transcript)
Dale Sanders, Chief Technology Officer
A Reference Architecture For Digital Health: The Health Catalyst Data Operating System (On Demand Webinar, Slides, and Transcript)
Dale Sanders, Chief Technology Officer
PowerPoint Slides
Would you like to use or share these concepts? Download this Healthcare Analytics Adoption Model presentation highlighting the key main points.